Authors:
Ladislav Jirsa
and
Lenka Pavelková
Affiliation:
Institute of Information Theory and Automation, Czech Republic
Keyword(s):
Sensor Condition, Abrupt Change, Signal Variance, Modelling, Uniform Distribution.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Change Detection
;
Data Engineering
;
Informatics in Control, Automation and Robotics
;
Real-Time Systems Control
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Modeling
Abstract:
A modular framework for monitoring complex systems contains blocks that evaluate condition of single signals,
typically of sensors. The signals are modelled and their values must be found within the prescribed
bounds. However, an abrupt change of the signal increases the estimated parameters’ variance, which raises
uncertainty of the sensor condition although it operates correctly. This increase affects the whole system in
evaluation of condition uncertainty. The solution must be fast and simple, because of runtime application
requirements. The signal is modelled by a static model with uniform noise, variance increase is tested and if
detected, the model memory is cleared. The fast and efficient algorithm is demonstrated on industrial rolling
data. The method prevents the parameters’ variance from the artificial increase.